Developing Youth-informed and Quality-aware Spatial Accessibility Measures to Urban Parks Using a Survey-based 2SFCA Method in London, Ontario and Halifax, Nova Scotia
Bibliographic record
Abstract
Park-related research has gained much attention in recent years, yet not enough studies have focused on the inequity of park accessibility and quality. These are crucial elements that influence youth’s park use, which in turn influence their physical, mental, and social development. Existing literature uses park size as the supply level to examine park accessibility but fails to consider any other park characteristics (e.g., amenities, general condition). This research developed youth-informed and quality-aware measures to consider the influence on park attraction by its quality and size rather than size only. This was implemented by consulting a youth advisory council to determine the relative importance of park features and the travel threshold used in the analysis to better understand park attractiveness for youth. Then, an accessibility score for each population unit is computed to represent the level of park accessibility, using the two-step floating catchment area (2SFCA) method. The proposed method can better differentiate higher accessibility from lower accessibility, providing more detailed accessibility results. The social equity analysis results indicated that median household income was not strongly correlated with the level of park accessibility. The research outcomes bring critical insights for park planners to improve park and recreational facilities in the city and promote healthy living among youth.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".